import pandas as pd
import numpy as np
import statistics #mathematical statistics
import matplotlib.pyplot as plt
import plotly.express as px #create entire figures
import seaborn as sns #data visuals

#html export
import plotly.io as pio
pio.renderers.default = 'notebook'

df_psy = pd.read_csv('psy_data_setEN.csv')
df_psy

	Person	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
0	1	16	Never	Hetero	Other	1.0	7.0	7.0	7.0	7.0	5.0	1.0	4.0	2.0	4.0	4.0
1	2	16	Never	New Types	Christian Other	6.0	4.0	7.0	4.0	7.0	7.0	1.0	4.0	2.0	4.0	4.0
2	3	17	Never	Homo	Christian	2.0	2.0	2.0	6.0	5.0	5.0	3.0	3.0	4.0	3.0	4.0
3	4	13	Never	New Types	Christian	1.0	7.0	4.0	4.0	6.0	1.0	6.0	2.0	2.0	2.0	1.0
4	5	19	Never	Hetero	Muslim	2.0	3.0	6.0	5.0	5.0	6.0	3.0	2.0	3.0	4.0	4.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39774	39772	21	Never	New Types	Muslim	4.0	5.0	7.0	6.0	4.0	7.0	4.0	3.0	3.0	4.0	4.0
39775	39773	48	Married	Hetero	Christian Other	6.0	7.0	5.0	3.0	6.0	1.0	5.0	2.0	2.0	1.0	2.0
39776	39774	20	Never	Hetero	Protestant	1.0	5.0	7.0	5.0	3.0	5.0	3.0	3.0	2.0	4.0	4.0
39777	39775	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0
39778	39775	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0

39779 rows × 16 columns

Data Wrangling involves:

1. Dropping Columns
2. Null values
3. Duplicated Values
4. Outliers
5. Calitative Variables

#dropping person column
df_psy = df_psy.drop(df_psy.columns[0], axis =1)
df_psy

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
0	16	Never	Hetero	Other	1.0	7.0	7.0	7.0	7.0	5.0	1.0	4.0	2.0	4.0	4.0
1	16	Never	New Types	Christian Other	6.0	4.0	7.0	4.0	7.0	7.0	1.0	4.0	2.0	4.0	4.0
2	17	Never	Homo	Christian	2.0	2.0	2.0	6.0	5.0	5.0	3.0	3.0	4.0	3.0	4.0
3	13	Never	New Types	Christian	1.0	7.0	4.0	4.0	6.0	1.0	6.0	2.0	2.0	2.0	1.0
4	19	Never	Hetero	Muslim	2.0	3.0	6.0	5.0	5.0	6.0	3.0	2.0	3.0	4.0	4.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39774	21	Never	New Types	Muslim	4.0	5.0	7.0	6.0	4.0	7.0	4.0	3.0	3.0	4.0	4.0
39775	48	Married	Hetero	Christian Other	6.0	7.0	5.0	3.0	6.0	1.0	5.0	2.0	2.0	1.0	2.0
39776	20	Never	Hetero	Protestant	1.0	5.0	7.0	5.0	3.0	5.0	3.0	3.0	2.0	4.0	4.0
39777	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0
39778	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0

39779 rows × 15 columns

#duplicated values
duplicates = df_psy.duplicated().sum()
duplicates

16

#Number of rows on data
len(df_psy)

39779

#Percentage of duplicated values
(duplicates/len(df_psy))*100

0.04022222780864275

<1% of data is duplicated hence safe to delete these duplicates

# duplicates
duplicates = df_psy[df_psy.duplicated]
duplicates

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
5840	20	Never	Hetero	Muslim	4.0	7.0	1.0	5.0	6.0	1.0	6.0	1.0	1.0	1.0	1.0
5958	19	Never	New Types	Muslim	5.0	6.0	7.0	7.0	7.0	7.0	4.0	4.0	3.0	2.0	4.0
8778	20	Never	New Types	Muslim	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	2.0	2.0
13331	18	Never	Hetero	Muslim	1.0	1.0	7.0	7.0	7.0	7.0	1.0	4.0	4.0	4.0	4.0
19915	23	Never	Hetero	Muslim	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0
23087	18	Never	Hetero	Muslim	1.0	1.0	7.0	7.0	7.0	7.0	1.0	4.0	4.0	4.0	4.0
23201	18	Never	Hetero	Muslim	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0
24848	22	Never	New Types	Muslim	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
26074	28	Never	Hetero	Muslim	6.0	6.0	5.0	4.0	6.0	5.0	6.0	2.0	1.0	1.0	2.0
28382	18	Never	New Types	Muslim	1.0	1.0	1.0	1.0	1.0	1.0	1.0	4.0	4.0	4.0	4.0
32458	23	Never	Hetero	Muslim	5.0	6.0	6.0	5.0	5.0	5.0	5.0	3.0	2.0	2.0	3.0
38016	21	Never	New Types	Muslim	1.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	2.0	2.0	2.0
38951	19	Never	Hetero	Muslim	2.0	2.0	7.0	6.0	5.0	7.0	2.0	4.0	4.0	4.0	4.0
39148	18	Never	New Types	Muslim	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0
39753	15	Never	Hetero	Jewish	3.0	6.0	6.0	6.0	3.0	2.0	2.0	3.0	4.0	4.0	4.0
39778	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0

#dropping duplicates
df_psy = df_psy.drop_duplicates()

#Number of current duplicates
duplicates = sum(df_psy.duplicated())
duplicates

0

df_psy

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
0	16	Never	Hetero	Other	1.0	7.0	7.0	7.0	7.0	5.0	1.0	4.0	2.0	4.0	4.0
1	16	Never	New Types	Christian Other	6.0	4.0	7.0	4.0	7.0	7.0	1.0	4.0	2.0	4.0	4.0
2	17	Never	Homo	Christian	2.0	2.0	2.0	6.0	5.0	5.0	3.0	3.0	4.0	3.0	4.0
3	13	Never	New Types	Christian	1.0	7.0	4.0	4.0	6.0	1.0	6.0	2.0	2.0	2.0	1.0
4	19	Never	Hetero	Muslim	2.0	3.0	6.0	5.0	5.0	6.0	3.0	2.0	3.0	4.0	4.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39773	16	Never	Asexual	Atheist	2.0	3.0	5.0	5.0	5.0	3.0	3.0	2.0	3.0	3.0	4.0
39774	21	Never	New Types	Muslim	4.0	5.0	7.0	6.0	4.0	7.0	4.0	3.0	3.0	4.0	4.0
39775	48	Married	Hetero	Christian Other	6.0	7.0	5.0	3.0	6.0	1.0	5.0	2.0	2.0	1.0	2.0
39776	20	Never	Hetero	Protestant	1.0	5.0	7.0	5.0	3.0	5.0	3.0	3.0	2.0	4.0	4.0
39777	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0

39763 rows × 15 columns

There is a reduction in number of rows due to deletion of duplicated values

#info on the data
df_psy.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 39763 entries, 0 to 39777
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   age          39763 non-null  int64  
 1   married      39763 non-null  object 
 2   orientation  39762 non-null  object 
 3   religion     39761 non-null  object 
 4   Q1           39761 non-null  float64
 5   Q2           39758 non-null  float64
 6   Q3           39759 non-null  float64
 7   Q4           39761 non-null  float64
 8   Q5           39759 non-null  float64
 9   Q6           39759 non-null  float64
 10  Q7           39759 non-null  float64
 11  Q8           39758 non-null  float64
 12  Q9           39759 non-null  float64
 13  Q10          39760 non-null  float64
 14  Q11          39761 non-null  float64
dtypes: float64(11), int64(1), object(3)
memory usage: 4.9+ MB

# checking for null values
df_psy.isna()

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
0	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39773	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
39774	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
39775	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
39776	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
39777	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False

39763 rows × 15 columns

df_psy[df_psy.isna().any(axis=1)]

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
90	15	Never	New Types	NaN	1.0	NaN	NaN	6.0	NaN	6.0	NaN	NaN	NaN	4.0	4.0
137	20	Never	Hetero	Atheist	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
153	24	Never	Homo	Hindu	5.0	NaN	4.0	5.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN
225	19	Never	NaN	NaN	2.0	NaN	NaN	6.0	NaN	NaN	3.0	NaN	2.0	NaN	3.0
257	15	Never	Homo	Jewish	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4.0	2.0

1. df_psy.isna() returns a DataFrame of the same shape as df_psy, where each element is either True if the corresponding element in df_psy is NaN or null, or False otherwise. This operation checks for missing values in each element of the DataFrame.

2. .any(axis=1) is used to check if there are any True values across each row. By specifying axis=1, it checks for the presence of at least one True value in each row. The result is a boolean Series with the same number of rows as df_psy, where each element indicates whether the corresponding row has any missing values.

3. df_psy[df_psy.isna().any(axis=1)] is indexing df_psy using the boolean Series from the previous step. It selects only those rows from df_psy where the corresponding value in the boolean Series is True, meaning that row has at least one missing value.

The result is assigned to null, which will be another DataFrame containing only the rows from df_psy that have missing values in any of their elements.

null = df_psy[df_psy.isna().any(axis=1)]

#dropping null values
df_psy = df_psy.dropna()

df_psy

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
0	16	Never	Hetero	Other	1.0	7.0	7.0	7.0	7.0	5.0	1.0	4.0	2.0	4.0	4.0
1	16	Never	New Types	Christian Other	6.0	4.0	7.0	4.0	7.0	7.0	1.0	4.0	2.0	4.0	4.0
2	17	Never	Homo	Christian	2.0	2.0	2.0	6.0	5.0	5.0	3.0	3.0	4.0	3.0	4.0
3	13	Never	New Types	Christian	1.0	7.0	4.0	4.0	6.0	1.0	6.0	2.0	2.0	2.0	1.0
4	19	Never	Hetero	Muslim	2.0	3.0	6.0	5.0	5.0	6.0	3.0	2.0	3.0	4.0	4.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39773	16	Never	Asexual	Atheist	2.0	3.0	5.0	5.0	5.0	3.0	3.0	2.0	3.0	3.0	4.0
39774	21	Never	New Types	Muslim	4.0	5.0	7.0	6.0	4.0	7.0	4.0	3.0	3.0	4.0	4.0
39775	48	Married	Hetero	Christian Other	6.0	7.0	5.0	3.0	6.0	1.0	5.0	2.0	2.0	1.0	2.0
39776	20	Never	Hetero	Protestant	1.0	5.0	7.0	5.0	3.0	5.0	3.0	3.0	2.0	4.0	4.0
39777	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0

39758 rows × 15 columns

There is a reduction in number of rows due to deletion of null values from the Dataframe

OUTLIERS

Outliers are the extreme values within the dataset that deviates significantly from the rest of the (so-called normal)objects—either being much larger or significantly smaller.

Outliers can be detected using

1. *Visualization* i.e. Box Plots- a graphical representation that displays the distribution of numerical data through quartiles. & Scatter Plots
2. *Implementing mathematical formulas on the dataset*
3. *Using the statistical approach* e.g IQR (Inter Quartile Range)

#using boxplot to find outlier for Q8
var = df_psy['Q8']
plt.boxplot(var)
plt.show()

# using plotly express to check for outliers
px.box(var)

There are no outliers

px.box(var, points = 'all')# is using the Plotly Express library in Python to create a box plot with all data points displayed

Based on the box plot using Plotly:

1. Bottom horizontal line of box plot is minimum value which is 1
2. First horizontal line of rectangle shape of box plot is First quartile or 25% which is 2
3. Second horizontal line of rectangle shape of box plot is Second quartile or 50% or median which is 3
4. Third horizontal line of rectangle shape of box plot is third quartile or 75% which is 4
5. Top horizontal line of rectangle shape of box plot is maximum value which is still 4
6. Anything outside blue box plot is outlier data or erroneous data hence no outliers.

df_psy

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
0	16	Never	Hetero	Other	1.0	7.0	7.0	7.0	7.0	5.0	1.0	4.0	2.0	4.0	4.0
1	16	Never	New Types	Christian Other	6.0	4.0	7.0	4.0	7.0	7.0	1.0	4.0	2.0	4.0	4.0
2	17	Never	Homo	Christian	2.0	2.0	2.0	6.0	5.0	5.0	3.0	3.0	4.0	3.0	4.0
3	13	Never	New Types	Christian	1.0	7.0	4.0	4.0	6.0	1.0	6.0	2.0	2.0	2.0	1.0
4	19	Never	Hetero	Muslim	2.0	3.0	6.0	5.0	5.0	6.0	3.0	2.0	3.0	4.0	4.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39773	16	Never	Asexual	Atheist	2.0	3.0	5.0	5.0	5.0	3.0	3.0	2.0	3.0	3.0	4.0
39774	21	Never	New Types	Muslim	4.0	5.0	7.0	6.0	4.0	7.0	4.0	3.0	3.0	4.0	4.0
39775	48	Married	Hetero	Christian Other	6.0	7.0	5.0	3.0	6.0	1.0	5.0	2.0	2.0	1.0	2.0
39776	20	Never	Hetero	Protestant	1.0	5.0	7.0	5.0	3.0	5.0	3.0	3.0	2.0	4.0	4.0
39777	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0

39758 rows × 15 columns

num = df_psy.drop(['married', 'orientation', 'religion'], axis=1)
fig = px.box(num.melt(),y = "value", facet_col='variable', boxmode= 'overlay', color = 'variable')#plotting outliers for all variables
fig.update_yaxes(matches = None)

Outliers are in age, Q4, Q5 & Q11

1. Q4 min-1 max-7 outlier-33
2. Q5 min-1 max-7 outlier-71
3. Q11 min-1 max-4 outlier-30

#finding maximum age
max_age = df_psy['age'].max()
max_age

1998

This age is impractical

max_age = df_psy[df_psy['age']>100] #finding age outliers
max_age

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
5342	223	Never	Hetero	Muslim	7.0	6.0	3.0	2.0	1.0	2.0	6.0	2.0	1.0	1.0	1.0
10233	1996	Never	New Types	Muslim	5.0	7.0	7.0	7.0	5.0	3.0	5.0	4.0	4.0	3.0	4.0
14238	117	Never	Homo	Christian Other	3.0	1.0	7.0	6.0	3.0	6.0	2.0	4.0	3.0	4.0	3.0
21385	1998	Never	Asexual	Muslim	1.0	1.0	6.0	7.0	1.0	1.0	1.0	2.0	1.0	2.0	2.0
24900	115	Never	Homo	Christian	5.0	3.0	6.0	6.0	5.0	6.0	2.0	3.0	2.0	1.0	2.0
30029	1993	Married	Hetero	Muslim	4.0	7.0	7.0	7.0	7.0	6.0	2.0	4.0	4.0	3.0	4.0
33732	1991	Never	Hetero	Atheist	2.0	7.0	7.0	5.0	6.0	6.0	2.0	3.0	3.0	3.0	4.0

df_psy = df_psy[df_psy['age']<100] #deletes the 7 rows which are age outliers

Q = df_psy[df_psy['Q4']>8] #finding outlier in Q4
Q

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
34616	21	Never	Hetero	Atheist	3.0	7.0	3.0	33.0	7.0	1.0	5.0	3.0	4.0	4.0	4.0

The outlier in Q4 is 33 as according to the Plotly Express box plot the min value is 1 and max value 7

#new rows for new dataframe
df_psy = df_psy[df_psy['Q4']<8]
df_psy = df_psy[df_psy['Q5']<8]
df_psy = df_psy[df_psy['Q4']<5]

#plotting numeric variables without outliers
num = df_psy.drop(['married', 'orientation', 'religion'], axis=1)
fig = px.box(num.melt(),y = "value", facet_col='variable', boxmode= 'overlay', color = 'variable')#plotting outliers for all variables
fig.update_yaxes(matches = None)

NON-NUMERIC VARIABLES

df_psy

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
1	16	Never	New Types	Christian Other	6.0	4.0	7.0	4.0	7.0	7.0	1.0	4.0	2.0	4.0	4.0
3	13	Never	New Types	Christian	1.0	7.0	4.0	4.0	6.0	1.0	6.0	2.0	2.0	2.0	1.0
6	17	Never	Bi	Christian Other	2.0	6.0	5.0	2.0	1.0	3.0	5.0	1.0	2.0	3.0	2.0
7	29	Never	Bi	Atheist	7.0	4.0	5.0	2.0	2.0	3.0	5.0	1.0	1.0	1.0	1.0
10	15	Never	Hetero	Protestant	5.0	6.0	6.0	4.0	4.0	7.0	5.0	3.0	2.0	3.0	2.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39765	22	Never	Hetero	Muslim	1.0	7.0	2.0	4.0	6.0	1.0	4.0	1.0	1.0	2.0	3.0
39771	27	Never	Hetero	Christian	6.0	6.0	7.0	4.0	6.0	5.0	5.0	2.0	1.0	2.0	2.0
39772	16	Never	Hetero	Agnostic	6.0	4.0	5.0	3.0	5.0	2.0	5.0	3.0	2.0	2.0	4.0
39775	48	Married	Hetero	Christian Other	6.0	7.0	5.0	3.0	6.0	1.0	5.0	2.0	2.0	1.0	2.0
39777	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0

14313 rows × 15 columns

#Analyzing possible outliers in non-numeric variables making sure they are proper
df_psy.married.unique()

array(['Never', 'Married', 'Previously', 'Second'], dtype=object)

df_psy.orientation.unique()

array(['New Types', 'Bi', 'Hetero', 'Homo', 'Asexual'], dtype=object)

df_psy.religion.unique()

array(['Christian Other', 'Christian', 'Atheist', 'Protestant',
       'Agnostic', 'Other', 'Jewish', 'Hindu', 'Buddhist', 'Muslim',
       'Mormon', 'Sikh'], dtype=object)

#changing to a numerical variable using dictionary
matrimonial = {
    'Never' : 1,
    'Previously' : 2,
    'Married' : 3, 
    'Second' : 4
}
df_psy['MarriedN'] = df_psy['married'].map(matrimonial)#builds a new column and maps to the dictionary

sexuality = {
    'New Types' : 1,
    'Bi' : 2,
    'Hetero' : 3, 
    'Homo' : 4,
    'Asexual' : 5
}
df_psy['OrientationN'] = df_psy['orientation'].map(sexuality)#builds a new column and maps to the dictionary

religia = {
    'Christian Other' : 1,
    'Christian' : 2,
    'Atheist' : 3, 
    'Protestant' : 4,
    'Agnostic' : 5,
    'Other' : 6,
    'Jewish' : 7,
    'Hindu' : 8,
    'Buddhist' : 9,
    'Muslim': 10,
    'Mormon' : 11,
    'Sikh' : 12
}
df_psy['ReligiaN'] = df_psy['religion'].map(religia)#builds a new column and maps to the dictionary

df_psy

	age	married	orientation	religion	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	MarriedN	OrientationN	ReligiaN
1	16	Never	New Types	Christian Other	6.0	4.0	7.0	4.0	7.0	7.0	1.0	4.0	2.0	4.0	4.0	1	1	1
3	13	Never	New Types	Christian	1.0	7.0	4.0	4.0	6.0	1.0	6.0	2.0	2.0	2.0	1.0	1	1	2
6	17	Never	Bi	Christian Other	2.0	6.0	5.0	2.0	1.0	3.0	5.0	1.0	2.0	3.0	2.0	1	2	1
7	29	Never	Bi	Atheist	7.0	4.0	5.0	2.0	2.0	3.0	5.0	1.0	1.0	1.0	1.0	1	2	3
10	15	Never	Hetero	Protestant	5.0	6.0	6.0	4.0	4.0	7.0	5.0	3.0	2.0	3.0	2.0	1	3	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
39765	22	Never	Hetero	Muslim	1.0	7.0	2.0	4.0	6.0	1.0	4.0	1.0	1.0	2.0	3.0	1	3	10
39771	27	Never	Hetero	Christian	6.0	6.0	7.0	4.0	6.0	5.0	5.0	2.0	1.0	2.0	2.0	1	3	2
39772	16	Never	Hetero	Agnostic	6.0	4.0	5.0	3.0	5.0	2.0	5.0	3.0	2.0	2.0	4.0	1	3	5
39775	48	Married	Hetero	Christian Other	6.0	7.0	5.0	3.0	6.0	1.0	5.0	2.0	2.0	1.0	2.0	3	3	1
39777	26	Never	Hetero	Muslim	6.0	3.0	5.0	3.0	5.0	5.0	1.0	2.0	2.0	1.0	3.0	1	3	10

14313 rows × 18 columns

# finding out the total of each religion in our dataset
df_psy['religion'].value_counts()

Muslim             8779
Christian          1123
Atheist            1023
Agnostic            941
Christian Other     787
Other               562
Protestant          523
Hindu               267
Buddhist            177
Jewish               59
Mormon               44
Sikh                 28
Name: religion, dtype: int64

# plotting a plotly express pie chart for number of each religion
religion_counts = df_psy['religion'].value_counts()

fig = px.pie(names=religion_counts.index, values=religion_counts.values)

fig.update_layout(title='Religion Distribution')

fig.show()

# finding out the total of each sexual orientation value
df_psy['orientation'].value_counts()

Hetero       8926
New Types    2443
Bi           1630
Homo          668
Asexual       646
Name: orientation, dtype: int64

# plotting a plotly express bar graph for each sexual orientation
sexuality_counts = pd.Series(df_psy['orientation'].value_counts()) 

fig = px.bar(x=sexuality_counts.index, y=sexuality_counts, title='Sexuality count')

fig.update_layout(yaxis_title = 'People')

fig.show()